Bulk_Density
Harnay
2025-01-20
Load Libraries
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library(ggmap)## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## Stadia Maps' Terms of Service: <https://stadiamaps.com/terms-of-service/>
## OpenStreetMap's Tile Usage Policy: <https://operations.osmfoundation.org/policies/tiles/>
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## align_plotsLoad data and metadata
#load wax data
wax.data <- read.csv("Data/test_bulk_density_data.csv", header=TRUE)
colnames(wax.data) <- c("Sample.ID", "DW.clean.g", "DW.wax.g", "BW.wax.g", "Temperature.C", "Notes")
wax.data$Sample.ID <- as.character(wax.data$Sample.ID)
wax.data <- na.omit(wax.data)
nrow(wax.data)==length(unique(wax.data$Sample.ID))## [1] FALSEduplicated_names <- wax.data[duplicated(wax.data$Sample.ID), ]
print(duplicated_names)## Sample.ID DW.clean.g DW.wax.g BW.wax.g Temperature.C
## 45 3685 8.470 8.965 4.824 20.09
## 121 3637 9.671 10.224 5.308 20.06
## 146 3838 6.228 6.550 3.018 20.06
## 187 3834 5.672 6.031 3.264 20.03
## 861 2050 6.196 6.566 0.515 20.01
## 941 938 8.011 8.469 4.117 20.01
## 1002 637 1.910 2.040 1.052 19.98
## Notes
## 45 duplicated, Pierrick has page
## 121 duplicated, page74
## 146 duplicated, Pierrick has page
## 187 duplicated, Pierrick has page
## 861 duplicated, page 101
## 941 duplicated, page 104
## 1002species.id <- read.csv("Data/test_Haplotype_data.csv", header=TRUE)
species.id$Species <- factor(species.id$Species)
species.id$Sample.ID <- as.character(species.id$Sample.ID)
metadata <- read.csv("/Users/hputnam/MyProjects/Pocillopora-Lagoon-Abundance/Data/POC_ID_RA_list.csv", header=TRUE) %>%
select(c("Number", "Stage", "Timing", "Site", "Transect"))
colnames(metadata) <- c("Sample.ID", "Stage", "Timing", "Site", "Transect")
metadata$Sample.ID <- as.character(metadata$Sample.ID)#Calculate surface area (Volume enclosed) using the standard curve
#View the range of DW.clean.g
range(wax.data$DW.clean.g)## [1] 0.023 31.154boxplot(wax.data$DW.clean.g)
hist(wax.data$DW.clean.g)
#View the range of DW.wax.g
range(wax.data$DW.wax.g)## [1] 0.032 32.007boxplot(wax.data$DW.wax.g)
hist(wax.data$DW.wax.g)
wax.data <- wax.data %>%
mutate(Vol.enc = DW.wax.g - BW.wax.g) %>%
mutate(Bulk.Density = DW.clean.g/Vol.enc)
#check the range of bulk density values
#1.44 - 2.22 reported 10.7717/peerj.3191
range(wax.data$Bulk.Density)## [1] -0.2958015 3.7469235boxplot(wax.data$Bulk.Density)
hist(wax.data$Bulk.Density)
Set species colors
species_colors <- c("P. tuahiniensis" = "#D55E00",
"P. meandrina" = "pink",
"P. meandrina P. grandis" = "#0072B2",
"P. verrucosa" = "#E69F00",
"P. grandis" = "#56B4E9",
"P. effusa" = "#009E73",
"P. acuta" = "#e63946")Plot by species and site
data <- left_join(wax.data, species.id, by= "Sample.ID")## Warning in left_join(wax.data, species.id, by = "Sample.ID"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 4 of `x` matches multiple rows in `y`.
## ℹ Row 515 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.data <- left_join(data, metadata, by= "Sample.ID")## Warning in left_join(data, metadata, by = "Sample.ID"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 4 of `x` matches multiple rows in `y`.
## ℹ Row 842 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.data$Species <- factor(data$Species, levels = c("P. tuahiniensis",
"P. meandrina",
"P. meandrina P. grandis",
"P. verrucosa",
"P. grandis",
"P. effusa",
"P. acuta"))
str(data)## 'data.frame': 1009 obs. of 20 variables:
## $ Sample.ID : chr "3968" "3847" "3841" "3834" ...
## $ DW.clean.g : num 30.62 6.59 9.7 5.73 5.73 ...
## $ DW.wax.g : num 31.88 6.96 10.22 6.16 6.16 ...
## $ BW.wax.g : num 16.16 3.35 5.03 2.89 2.89 ...
## $ Temperature.C : num 20.1 20.2 20.2 20.1 20.1 ...
## $ Notes.x : chr "" "" "" "duplicated, Pierrick has page" ...
## $ Vol.enc : num 15.72 3.6 5.19 3.27 3.27 ...
## $ Bulk.Density : num 1.95 1.83 1.87 1.75 1.75 ...
## $ Sanger.Plate.Name : chr NA "RAA_241009" "RAA_241009" "RAA_241007B" ...
## $ Sequence.ID : chr NA "RA0501" "RA0492" "RA0304" ...
## $ Plate.Well : chr NA "B4" "A3" "C3" ...
## $ gDNA.Extraction.Plate: chr NA "Plate015" "Plate015" "Plate013" ...
## $ Haplotype : chr NA "Haplotype 1a" "Haplotype 1a" "Haplotype 1a" ...
## $ Notes.y : chr NA "" "" "" ...
## $ Species : Factor w/ 7 levels "P. tuahiniensis",..: NA 3 3 3 3 3 3 2 3 3 ...
## $ X : chr NA "" "" "" ...
## $ Stage : chr "" "" "" "" ...
## $ Timing : chr "Post_bleach" "Pre_bleach" "Pre_bleach" "Pre_bleach" ...
## $ Site : chr "S15" "S14" "S14" "S14" ...
## $ Transect : chr "T2" "T2" "T2" "T2" ...
## - attr(*, "na.action")= 'omit' Named int [1:9] 711 712 713 714 715 716 717 718 719
## ..- attr(*, "names")= chr [1:9] "711" "712" "713" "714" ...#set site order
site_order <- c("S1","S2","S3","S4","S5","S6",
"S9","S11","S12","S14","S15","S18")
# Convert Site column to a factor with the custom order
data$Site <- factor(data$Site, levels = site_order)
# Check the structure to confirm the changes
str(data)## 'data.frame': 1009 obs. of 20 variables:
## $ Sample.ID : chr "3968" "3847" "3841" "3834" ...
## $ DW.clean.g : num 30.62 6.59 9.7 5.73 5.73 ...
## $ DW.wax.g : num 31.88 6.96 10.22 6.16 6.16 ...
## $ BW.wax.g : num 16.16 3.35 5.03 2.89 2.89 ...
## $ Temperature.C : num 20.1 20.2 20.2 20.1 20.1 ...
## $ Notes.x : chr "" "" "" "duplicated, Pierrick has page" ...
## $ Vol.enc : num 15.72 3.6 5.19 3.27 3.27 ...
## $ Bulk.Density : num 1.95 1.83 1.87 1.75 1.75 ...
## $ Sanger.Plate.Name : chr NA "RAA_241009" "RAA_241009" "RAA_241007B" ...
## $ Sequence.ID : chr NA "RA0501" "RA0492" "RA0304" ...
## $ Plate.Well : chr NA "B4" "A3" "C3" ...
## $ gDNA.Extraction.Plate: chr NA "Plate015" "Plate015" "Plate013" ...
## $ Haplotype : chr NA "Haplotype 1a" "Haplotype 1a" "Haplotype 1a" ...
## $ Notes.y : chr NA "" "" "" ...
## $ Species : Factor w/ 7 levels "P. tuahiniensis",..: NA 3 3 3 3 3 3 2 3 3 ...
## $ X : chr NA "" "" "" ...
## $ Stage : chr "" "" "" "" ...
## $ Timing : chr "Post_bleach" "Pre_bleach" "Pre_bleach" "Pre_bleach" ...
## $ Site : Factor w/ 12 levels "S1","S2","S3",..: 11 10 10 10 10 10 10 10 10 10 ...
## $ Transect : chr "T2" "T2" "T2" "T2" ...
## - attr(*, "na.action")= 'omit' Named int [1:9] 711 712 713 714 715 716 717 718 719
## ..- attr(*, "names")= chr [1:9] "711" "712" "713" "714" ...bulk.density.plot.raw <- data %>%
ggplot(aes(x = Site, y = Bulk.Density, color = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point() + # Plot the data points
scale_color_manual(values = species_colors) + # Apply the manual color scale
facet_wrap(~ Species) + # Facet by Species
theme_bw()+
labs(title="All Raw Data",
x = "Species",
y = "Bulk Density g/cm2"
)
bulk.density.plot.raw
#remove outliers above 2.93
filtered_data <- data %>%
filter(Bulk.Density <2.93, na.rm = TRUE) %>%
filter(Bulk.Density >0, na.rm = TRUE)
bulk.density.plot.outlier.removed <- filtered_data %>%
ggplot(aes(x = Site, y = Bulk.Density, color = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point() + # Plot the data points
scale_color_manual(values = species_colors) + # Apply the manual color scale
facet_wrap(~ Species) + # Facet by Species
theme_bw()+
labs(title="Outlier Removed",
x = "Species",
y = "Bulk Density g/cm2"
)
bulk.density.plot.outlier.removed
bulk.density.plot.outlier.removed.species <- filtered_data %>%
ggplot(aes(x = Site, y = Bulk.Density, color = Species, group = Species)) + # Map color to Species
geom_point(position = position_dodge(width = 0.9), size = 1, alpha = 0.2) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 3, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
) +
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
scale_color_manual(values = species_colors) + # Use your manual color palette for the species
theme_classic() + # Classic theme for cleaner look
labs(
title = "Outlier Removed",
x = "Site",
y = "Bulk Density g/cm²"
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Display the plot
#bulk.density.plot.outlier.removed.speciesView a single site S1
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S1") %>%
filter(Species != "NA")
bulk.density.plot.S1 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S1",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S1## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
View a single site S2
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S2") %>%
filter(Species != "NA")
bulk.density.plot.S2 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S2",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S2## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S3
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S3") %>%
filter(Species != "NA")
bulk.density.plot.S3 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S3",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S3## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S4
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S4") %>%
filter(Species != "NA")
bulk.density.plot.S4 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S4",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S4## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S5
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S5") %>%
filter(Species != "NA")
bulk.density.plot.S5 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S5",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S5## Warning: Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
## Removed 1 row containing non-finite outside the scale range (`stat_summary()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S6
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S6") %>%
filter(Species != "NA")
bulk.density.plot.S6 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S6",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S6## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
View a single site S9
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S9") %>%
filter(Species != "NA")
bulk.density.plot.S9 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S9",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S9## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
View a single site S11
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S11") %>%
filter(Species != "NA")
bulk.density.plot.S11 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S11",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S11## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S12
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S12") %>%
filter(Species != "NA")
bulk.density.plot.S12 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S12",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S12## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S14
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S14") %>%
filter(Species != "NA")
bulk.density.plot.S14 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S14",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S14## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S15
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S15") %>%
filter(Species != "NA")
bulk.density.plot.S15 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S15",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S15## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
View a single site S18
#remove max outlier
site_filtered_data <- data %>%
filter(Bulk.Density != max(data$Bulk.Density, na.rm = TRUE)) %>%
filter(Site == "S18") %>%
filter(Species != "NA")
bulk.density.plot.S18 <- site_filtered_data %>%
ggplot(aes(x = Species, y = Bulk.Density, color = Species, group = Species)) + # Add 'color = Species' to map colors to the Species variable
geom_point(position = position_dodge(width = 0.9), size = 2, alpha = 0.3) + # Plot the individual data points
stat_summary(
fun = "mean", # Compute the mean
geom = "point", # Add the mean as a point
size = 5, # Size of the mean points
shape = 18, # Diamond shape for mean points
aes(color = Species), # Make the mean points the same color as the species
position = position_dodge(width = 0.9) # Dodge means to align with the individual points
)+
scale_color_manual(values = species_colors) + # Apply the manual color scale
geom_line(
stat = "summary", # Use the summary statistics (mean) to plot the line
fun = "mean", # Line connects the means
aes(group = Species, color = Species), # Ensure the line connects the means of each species
size = 1, # Thickness of the line
position = position_dodge(width = 0.9)) +
ylim(1.0, 2.4)+
theme_bw()+
theme(legend.position ="none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_line())+
labs(title="S18",
y = "Bulk Density g/cm2"
)
bulk.density.plot.S18## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
plot to pdf
# Save the plots to a PDF in one column
pdf("Output/BW.plots.pdf", height = 16, width = 10) # Set dimensions for the PDF
grid.arrange(bulk.density.plot.raw,
bulk.density.plot.outlier.removed,
bulk.density.plot.outlier.removed.species,
ncol = 1) # Arrange in 1 column
dev.off() # Close the PDF device## quartz_off_screen
## 2# Save the plots to a PDF in one column
pdf("Output/BDSite.plots.pdf", height = 18, width = 12) # Set dimensions for the PDF
grid.arrange(bulk.density.plot.S1,bulk.density.plot.S2,
bulk.density.plot.S3,bulk.density.plot.S4,
bulk.density.plot.S5,bulk.density.plot.S6,
bulk.density.plot.S9,bulk.density.plot.S11,
bulk.density.plot.S12,bulk.density.plot.S14,
bulk.density.plot.S15, bulk.density.plot.S18,
ncol = 3) # Arrange in 1 column## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.dev.off() # Close the PDF device## quartz_off_screen
## 2Make the Moorea Island Map
register_google(key = "AIzaSyAJHWQg-KMSzffFNWaO1zAakoBz-klFhIg") ### use your own API
# location
Moorea<-data.frame(lon = -149.83246425684064, lat = -17.531092816791094)
#Map base
M1<-get_map(Moorea,zoom = 12, maptype = 'satellite')## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=-17.531093,-149.832464&zoom=12&size=640x640&scale=2&maptype=satellite&language=en-EN&key=xxx-KMSzffFNWaO1zAakoBz-klFhIg>bbx <- c(left=-149.802,bottom= -17.480,right=-149.805,top=-17.475)
x <- c(bbx["left"], bbx["left"], bbx["right"], bbx["right"])
y <- c(bbx["bottom"], bbx["top"], bbx["top"], bbx["bottom"])
df <- data.frame(x, y)
Mooreamap<-ggmap(M1)+
scalebar(x.min = -149.90, x.max = -149.05,y.min = -17.4, y.max = -18.0,
model = 'WGS84', box.fill = c("yellow", "white"), st.color = "white",
location = "bottomright", transform = TRUE, dist_unit = "km", dist = 10) +
#geom_polygon(aes(x=x, y=y), data=df, color="yellow", fill=NA) +
#geom_text(data = isledata, aes(x=Long, y=Lat, label=island),vjust =0,size=4, color = 'yellow')+
ggtitle('A')+
xlab("")+
ylab("")
Mooreamap## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_text()`).
Make Colony and Transect Map
sitedata <- read.csv("Data/transect.site.coords.csv")
str(sitedata)## 'data.frame': 12 obs. of 3 variables:
## $ long : num -150 -150 -150 -150 -150 ...
## $ lat : num -17.5 -17.6 -17.6 -17.6 -17.5 ...
## $ site.name: chr "S1" "S11" "S12" "S14" ...labels <- sitedata$site.name
#Map base
#M3<-get_map(NS,zoom = 18, maptype = 'satellite')
MooreaSitemap<-ggmap(M1)+
scalebar(x.min = -149.75, x.max = -149.79,y.min = -17.60, y.max = -17.58,
model = 'WGS84', box.fill = c("white", "white"), st.color = "white",st.dist=0.3,
location = "bottomright", transform = TRUE, dist_unit = "km", dist = 1) +
geom_point(data = sitedata, mapping = aes(x=long, y=lat), size=2, color="yellow")+
geom_text(data = sitedata, aes(x=long, y=lat, label=site.name),vjust = -2,size=3, color="yellow")+
#geom_line(data = transects, mapping = aes(x=long, y=lat,group=grouping), size=0.5, color="black")+
#ggtitle('C')+
xlab("")+
ylab("")
MooreaSitemap
spatial mapping of plots around the island map
empty_plot <- ggplot() + theme_void()
legend_plot <- ggplot(data = data.frame(x = 1, y = 1, group = names(species_colors)), aes(x = x, y = y, color = group)) +
geom_point(size = 0) + # No actual points, just to display the legend
scale_color_manual(values = species_colors) + # Use your custom color palette
theme_void() + # Remove axes and background
theme(legend.title = element_blank()) + # Remove legend title
guides(color = guide_legend(override.aes = list(size = 5)))
legend_plot
grid_layout <- plot_grid(
bulk.density.plot.S1,bulk.density.plot.S2,bulk.density.plot.S3,bulk.density.plot.S4,bulk.density.plot.S5,
bulk.density.plot.S18,empty_plot,empty_plot,empty_plot,bulk.density.plot.S6,
bulk.density.plot.S15,empty_plot,empty_plot,empty_plot,bulk.density.plot.S9,
bulk.density.plot.S14, empty_plot,empty_plot,empty_plot,bulk.density.plot.S11,
empty_plot,empty_plot, bulk.density.plot.S12,empty_plot,legend_plot,
ncol = 5, nrow = 5
)## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?final_grid_with_map <- grid_layout +
draw_plot(MooreaSitemap, x = 0.2, y = 0.2, width = 0.6, height = 0.6) # Adjust size and position
final_grid_with_map 
ggsave("Output/grid_layout.pdf", final_grid_with_map, width = 10, height = 10)